gensai.aiA vector database stores content (text, images, audio) as numerical vectors, known as embeddings, that capture its meaning. It can retrieve semantically similar content even when the words differ, which makes it the foundation of semantic search and RAG architectures. Pinecone, FAISS and Weaviate are examples.
In practice at Gensai
Gensai relies on vector databases such as Pinecone and FAISS for its natural language search engines and document chatbots.
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Definition written with AI assistance, checked by Sébastien Lecocq, co-founder of Gensai.
Going beyond the definition?
From concept to project: Gensai builds custom, sovereign, GDPR-compliant AI solutions.
Related services: Chatbots and answer engines
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